About this role

Build and optimize end-to-end ML pipelines from data ingestion through deployment.
Implement and fine-tune LLMs, embeddings, and generative models for real-world use cases.
Develop efficient training and inference systems leveraging distributed compute.
Partner with data and product teams to translate ideas into measurable ML outcomes.
Contribute to model monitoring, evaluation, and continual learning frameworks.
Establish best practices for model versioning, reproducibility, and scalability.
Move quickly between experimentation and production deployments, balancing research and engineering rigor.

3–10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer.
Strong ML fundamentals: data preprocessing, feature engineering, model training, and optimization.
Proficiency in Python and at least one deep learning framework: PyTorch, TensorFlow, or JAX.
Experience with distributed training/inference and cloud ML infrastructure (AWS, GCP, or Azure).
Hands-on experience building end-to-end ML pipelines from data ingestion to production deployment.
Familiarity with MLOps tooling (e.g., Weights & Biases, MLflow) and model monitoring approaches.
Comfortable working with large datasets and high-throughput systems.
Bias for action, ability to work autonomously, and eagerness to build systems from scratch.
Willingness to work onsite in Mountain View, CA.

Founding Engineer – Machine Learning

Key skills